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IJCNN
2006
IEEE
15 years 5 months ago
Adaptation of Artificial Neural Networks Avoiding Catastrophic Forgetting
— In connectionist learning, one relevant problem is “catastrophic forgetting” that may occur when a network, trained with a large set of patterns, has to learn new input pat...
Dario Albesano, Roberto Gemello, Pietro Laface, Fr...
RECOMB
1999
Springer
15 years 3 months ago
Fast detection of common geometric substructure in proteins
We consider the problem of identifying common three-dimensional substructures between proteins. Our method is based on comparing the shape of the α-carbon backbone structures of ...
L. Paul Chew, Daniel P. Huttenlocher, Klara Kedem,...
BMCBI
2007
153views more  BMCBI 2007»
14 years 11 months ago
Analysis of nanopore detector measurements using Machine-Learning methods, with application to single-molecule kinetic analysis
Background: A nanopore detector has a nanometer-scale trans-membrane channel across which a potential difference is established, resulting in an ionic current through the channel ...
Matthew Landry, Stephen Winters-Hilt
NIPS
2003
15 years 1 months ago
Max-Margin Markov Networks
In typical classification tasks, we seek a function which assigns a label to a single object. Kernel-based approaches, such as support vector machines (SVMs), which maximize the ...
Benjamin Taskar, Carlos Guestrin, Daphne Koller
CVPR
2008
IEEE
16 years 1 months ago
Coherent image annotation by learning semantic distance
Conventional approaches to automatic image annotation usually suffer from two problems: (1) They cannot guarantee a good semantic coherence of the annotated words for each image, ...
Tao Mei, Yong Wang, Xian-Sheng Hua, Shaogang Gong,...